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Scaling up deep reinforcement learning for multi-domain dialogue systems
2017
2017 International Joint Conference on Neural Networks (IJCNN)
Standard deep reinforcement learning methods such as Deep Q-Networks (DQN) for multiple tasks (domains) face scalability problems due to large search spaces. This paper proposes a three-stage method for multi-domain dialogue policy learning-termed NDQN, and applies it to an informationseeking spoken dialogue system in the domains of restaurants and hotels. In this method, the first stage does multi-policy learning via a network of DQN agents; the second makes use of compact state
doi:10.1109/ijcnn.2017.7966275
dblp:conf/ijcnn/CuayahuitlYWC17
fatcat:o4ijg3ryrfde5emfj2olnu62km